DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2026-02-04 has been entered.
Status of the Claims
Claims 1-20 remain under examination in this Office Action.
Response to Arguments
Applicant's arguments filed 2026-02-04 (“Remarks”) have been fully considered but they are not persuasive.
Applicant alleges that: “Nowhere does Park mentions [sic] logical space or using logical space to predict signal strength in a physical space. Therefore, Park does not teach or suggest: (a) predicting, by a machine learning process, signal strengths of signals sent by one or more APs and received by one or more STAs, the machine learning process using the logical space, and each STA being in a location of the physical space; or (b) that predicting, by the machine learning process, the signal strengths of the signals sent by the one or more APs and received by the one or more STAs includes: (i) generating a heatmap for at least one of the one or more APs of the physical space, (ii) that generating the heatmap includes measuring a signal strength of signals from the at least one of the one or more APs observed by one or more STAs at different locations of the physical space, and (iii) predicting, by the machine learning process using the measured signal strength from the at least one of the one or more APs observed by the one or more STAs at the different locations of the physical space, the signal strengths of the signals sent by the one or more APs from different positions in the physical space and received by the one or more STAs the different locations of the physical space” (see Remarks, pp.8-9).
Regarding Applicant’s assertion that “Nowhere does Park mentions [sic] logical space” (Remarks, p.8): as pointed out in the previous Office Action, Park mentions using “a machine-learning model…to translate a floor plan to a radio propagation map (e.g., a heatmap)” (Park, ¶0045). The broadest reasonable interpretation of a “logical space” includes any digital (i.e., “logical”) representation of a space, which would make Park’s floor plan(s), radio propagation map(s), heatmap(s), etc. (see Park, Figs. 1C-D, 2A-B, and ¶0045) example(s) of a “logical space”. (See also Applicant’s specification, which states that “The AP coordinator 102 may translate the physical space into the logical space by creating heatmaps for one or more AP locations” (Detailed Description, ¶0050) and “The AP coordinator 102 may also create the logical space by creating multiple heatmaps and/or some other graph with signal strengths of APs at different positions. FIG. 4A is a block diagram 400 of a heatmap generated for the first AP 110” (Detailed Description, ¶0034).) Thus, under the broadest reasonable interpretation, Park discloses using a machine learning process and wherein the machine learning process uses the logical space (Park, Figs. 1C-D, 2A-b, ¶0045).
Regarding Applicant’s argument that “Nowhere does Park mentions [sic] … using logical space to predict signal strength in a physical space”, “Therefore, Park does not teach or suggest [the further limitations of the independent claims]”, and “Malboubi does not overcome Park’s and Kong’s deficiencies as Malboubi does not teach or suggest the aforementioned recitation” (see Remarks, pp.8-9): one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). It is the combination of references—Kong, Park, Malboubi, and Kappes—that teaches the limitations of the independent claims (please see Claim Rejections – 35 USC § 103).
Regarding Applicant’s argument that “Combining Kong with Park and Malboubi would not have led to the claimed subject matter because Kong, Prak [sic], and Malboubi, either individually or in combination, at least does not disclose [the limitations of the independent claims]” (see Remarks, p.9): the rejection under 35 U.S.C. 103 is based upon the teachings, in combination, of Kong, Park, Malboubi, and Kappes. Thus, this argument fails to comply with 37 CFR 1.111(b) because it amounts to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Thus, the rejections under 35 U.S.C. 103 are maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11,849,336 to Kong et al. (“Kong”) in view of U.S. Patent Publication No. 2023/0059954 to Park et al. (“Park”), U.S. Patent No. 12,302,157 to Malboubi et al. (“Malboubi”), and U.S. Patent Publication No. 2023/0328538 to Kappes et al. (“Kappes”).
As to claim 8 (and similarly applied to claims 1 and 15), Kong discloses a system comprising: a memory storage; and a processing unit couple to the memory storage (Figs. 2 and 24; Col. 49: lines 45-58), wherein the processing unit is operative to: translate a physical space into a logical space (Figs. 2, 6, 9(c), 15, and 22; Col. 25: lines 35-41, "the terminal device obtains the first data based on the method, and generates the first floor plan by using the first data. The floor plan generation method may be a sound wave detection method, or the first floor plan may be generated by using a sensor, a depth camera, or the like built in the terminal device"), wherein the physical space is being evaluated for Access Point (AP) coordination (Figs. 2, 6, 15, and 22; Col. 26: lines 29-35, "determine, by using the first floor plan, an appropriate quantity of APs recommended to be deployed and appropriate locations of the APs recommended to be deployed") predict…signal strengths of signals sent by one or more APs and received by one or more Stations (STAs) (Figs. 12(d), 13(c), and 14(f); Col. 31: lines 8-15, "The wifi heatmap is used to display strengths of wireless signals in different areas in the house in which the AP needs to be deployed currently according to the recommendation of the terminal device". See also Col. 31: line 54 through Col. 32: line 4; Examiner notes that Kong discloses recommended placements of AP deployment within the physical space, and that following operations may optionally display a heatmap that shows a prediction of signal strengths sent by the one or more APs (and received by one or more stations) throughout the physical space according to the recommended AP placement), and wherein each STA is in a location of the physical space (Fig. 1; Col. 9: lines 3-9); and…evaluate one or more AP placements based on the signal strengths (Figs. 6, 15, and 22; Col. 45: lines 10-15, "the location at which the master AP is deployed, and the quantity of to-be-deployed sub-APs and the locations of the sub-APs are determined"); and determine a recommended AP placement based on the evaluation (Figs. 2, 6, and 15; Col. 26: lines 29-35, "determine, by using the first floor plan, an appropriate quantity of APs recommended to be deployed and appropriate locations of the APs recommended to be deployed").
Kong does not disclose: using a machine learning process; wherein the machine learning process uses the logical space; wherein the processing unit being operative to predicting, using the machine learning process, the signal strengths of the signals sent by the one or more APs and received by the one or more STAs comprises the processing unit being operative to: generate a heatmap for at least one of the one or more APs of the physical space, wherein generating the heatmap comprises measuring a signal strength of signals from the at least one of the one or more APs observed by one or more STAs at different locations of the physical space, or predict, using the machine learning process using the measured signal strength from the at least one of the one or more APs observed by the one or more STAs at the different locations of the physical space, the signal strengths of the signals sent by the one or more APs from different positions in the physical space and received by the one or more STAs the different locations of the physical space.
However, Park discloses: using a machine learning process (Figs. 1C-D, 2A-B; ¶0045, "A machine-learning model is used to translate a floor plan to a radio propagation map (e.g., a heatmap)") and wherein the machine learning process uses the logical space (Figs. 1C-D, 2A-B; ¶0045, "A machine-learning model is used to translate a floor plan to a radio propagation map (e.g., a heatmap)"; examiner notes this is a machine learning process using a floor plan (i.e., a logical space)).
Additionally, Malboubi discloses: wherein the processing unit being operative to predicting, using the machine learning process, the signal strengths of the signals sent by the one or more APs and received by the one or more STAs comprises the processing unit being operative to: generate a heatmap for at least one of the one or more APs of the physical space (Fig. 2B and Col. 5: lines 10-12) and predict, using the machine learning process…the signal strengths of the signals sent by the one or more APs from different positions in the physical space and received by the one or more STAs the different locations of the physical space (Fig. 2B, Col. 5: lines 31-50; and Col. 5: lines 58-61; "the PE2 engine can be used in a variety of applications, including…properties of a cell can be predicted, including…the cell RSRP/RSRQ profile").
Additionally, Kappes discloses: wherein generating the heatmap comprises measuring a signal strength of signals from the at least one of the one or more APs observed by one or more STAs at different locations of the physical space (¶0078), and…using the measured signal strength from the at least one of the one or more APs observed by the one or more STAs at the different locations of the physical space (¶0078).
Kong, Park, Malboubi, and Kappes are considered to be similar to the claimed invention because they are in one or more of the same fields of: network planning, e.g. coverage or traffic planning tools; network deployment, e.g. resource partitioning or cells structures; and/or arrangements, using machine learning or artificial intelligence, for maintenance, administration or management of data switching networks. As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kong to incorporate the teachings of Park to include: using a machine learning process and wherein the machine learning process uses the logical space. Doing so would "reduce lead time to determine an indoor radio transmitter distribution (e.g., the number of indoor radio transmitters, their placements and associated bill of materials (BOMs) for a given venue or building), thus they enable a network designer to produce BoMs and preliminary quotes to customers significantly faster. The embodiments also enable the network designer to significantly scale up the design and deployment of indoor radio transmitters when designing an indoor cellular network" (Park, ¶0008).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kong to incorporate the teachings of Malboubi to include: wherein the processing unit being operative to predicting, using the machine learning process, the signal strengths of the signals sent by the one or more APs and received by the one or more STAs comprises the processing unit being operative to: generate a heatmap for at least one of the one or more APs of the physical space and predict, using the machine learning process…the signal strengths of the signals sent by the one or more APs from different positions in the physical space and received by the one or more STAs the different locations of the physical space. Doing so would facilitate "the effective and efficient design, operation and troubleshooting of 5G and next-generation mobile networks…by developing procedures for monitoring and predicting cell performance; these procedures can be implemented using data-oriented Machine-Learning/Artificial-Intelligence (ML/AI) models. Training and building such ML/AI models generally requires large volumes of labeled training data; training effective ML models therefore can be challenging and costly. It is desirable to develop area-specific ML models that can be trained locally and instantiated at network edges to provide low-latency responses" (Malboubi, Col. 1: lines 16-27).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kong to incorporate the teachings of Kappes to include: wherein generating the heatmap comprises measuring a signal strength of signals from the at least one of the one or more APs observed by one or more STAs at different locations of the physical space, and…using the measured signal strength from the at least one of the one or more APs observed by the one or more STAs at the different locations of the physical space. Doing so would allow for "[running] diagnostic tests such as determining signal strength, signal quality, performance rating, actual throughput (upload/download speed) and/or latency associated with such locations/orientations to again, assist a user in determining optimal placement of the FWA device" (Kappes, ¶0044).
As to claim 9 (and similarly applied to claims 2 and 16), Kong in view of Park, Malboubi, and Kappes discloses the system of claim 8, wherein to translate the physical space into the logical space (Kong, Figs. 2, 6, 9(c), 15, and 22; Col. 25: lines 35-41) includes to create a heatmap for at least one of the one or more APs (Park, Figs. 1C-D, 2A-B; ¶0045, "A machine-learning model is used to translate a floor plan to a radio propagation map (e.g., a heatmap)").
As to claim 10 (and similarly applied to claims 3 and 17), Kong in view of Park, Malboubi, and Kappes discloses the system of claim 8, wherein the processing unit (Kong, Figs. 2 and 24; Col. 49: lines 45-58) is further operative to train the machine learning process (Park, Figs. 1C-D, 2A-B, 7; ¶0073, "The cGAN is trained using a plurality of data sets, each data set including one floor plan image and one radio propagation map produced for the one floor plan image") wherein the processing unit being operative to train the machine learning process comprises the processing unit being operative to: generate actual signal strengths using at least one of the one or more STAs (Park, Figs. 1C-D, 2A-B; ¶0036, "a discriminator network could be trained with radio propagation data collected from previously implemented indoor radio propagation designs as well as from radio measurements collected from current indoor field deployments"); and cause the machine learning process to compare the predicted signal strengths and the actual signal strengths (Malboubi, Figs. 2A-D; Col. 2: lines 3-23, "predicting a future performance of the cell, resulting in a predicted performance; and subsequently determining a current performance of the cell for comparing with the predicted performance. The method further includes iteratively executing, using the labeled training data, a training procedure for the ML model in accordance with the comparing, resulting in a trained ML model"; and Col. 5: line 44 through Col. 6: line 28).
As to claim 11 (and similarly applied to claims 4 and 18), Kong in view of Park, Malboubi, and Kappes discloses the system of claim 8, wherein the machine learning process is a deep neural network (Park, Figs. 2A-B; ¶0045, "The machine-learning model may be a conditional Generative Adversarial Network (cGAN)").
As to claim 12 (and similarly applied to claims 5 and 19), Kong in view of Park, Malboubi, and Kappes discloses the system of claim 8, wherein the processing unit (Kong, Figs. 2 and 24; Col. 49: lines 45-58) being operative to evaluate the one or more AP placements (Kong, Figs. 2 and ; Col. 26: lines 29-35, "determine, by using the first floor plan, an appropriate quantity of APs recommended to be deployed and appropriate locations of the APs recommended to be deployed") comprises the processing unit being operative to: determine device scores for APs included in the AP placements (Kong, Fig. 15; Col. 40: line 57 through Col. 41: line 23, "a difference between strength of a wireless signal transmitted by the AP 1 and overall wireless signal attenuation between a grid in which the AP 1 is located and the grid a is first calculated, and the difference is used as first signal strength; a difference between strength of a wireless signal transmitted by the AP 2 and overall wireless signal attenuation between a grid in which the AP 2 is located and the grid a is calculated, and the difference is used as second signal strength; and the first signal strength is compared with the second signal strength, and a larger value is selected as the strength of the wireless signal finally received in the grid a… a difference between the strength of the wireless signal transmitted by the master AP and the wireless signal attenuation between the grid in which the master AP is located and the another grid is used as the strength of the wireless signal finally received in each grid in the house"); and determine any one of (i) a sum of the device scores for the AP placements, (ii) a square root of the sum of the squared device scores for the AP placements, (iii) a minimum device score for the AP placements, or (iv) any combination of (i)-(iii) (Kong, Fig. 15; Col. 41: lines 24-41, "specific values of the first threshold and the second threshold may be preset by the terminal device. In this case, the terminal device determines the specific values of the first threshold and the second threshold based on the preset setting, and determines whether each grid meets a deployment requirement. Further, the specific values of the first threshold and the second threshold may be adjusted in a deployment process based on an actual requirement").
As to claim 13 (and similarly applied to claims 6 and 20), Kong in view of Park, Malboubi, and Kappes discloses the system of claim 12, wherein the processing unit (Kong, Figs. 2 and 24; Col. 49: lines 45-58) being operative to determine the recommended AP placement comprises the processing unit being operative to determine the recommended AP placement from the one or more AP placements (Kong, Figs. 2, 6, and 15; Col. 26: lines 29-35, "determine, by using the first floor plan, an appropriate quantity of APs recommended to be deployed and appropriate locations of the APs recommended to be deployed") based on any of (v) the sums of the device scores, (vi) the square roots of the sum of the squared device scores, (vii) the minimum device scores, or (viii) any combination of (v)-(vii) (Kong, Fig. 15; Col. 41: lines 7-23, "it is determined, based on a result of comparing the wireless signal strength finally received in each grid with the first threshold and the second threshold, whether a deployment requirement is met".
As to claim 14 (and similarly applied to claim 7), Kong in view of Park, Malboubi, and Kappes discloses the system of claim 13, wherein the processing unit (Kong, Figs. 2 and 24; Col. 49: lines 45-58) is further operative to determine an alternative recommended AP placement (Kong, Fig. 13(c); Col. 32: lines 43-56, " the interface for displaying the wifi heatmap may further include an option “End”. After receiving a tap operation for the option “End”, the terminal device ends the current AP deployment, and jumps to an interface before AP deployment starts"; Examiner notes that the device being further operative to jump "to an interface before AP deployment starts" allows the device to perform the AP placement recommendation operations again, thus the device is "further operative to determine an alternative recommended AP placement").
References Cited
Kappes, James et al. (2023). Fwa device self-installation application with dual sim capability (US 2023/0328538 A1). Filed 2022-04-08.
Kong, Fanhua et al. (2023). Wireless access point deployment method and apparatus (US 11,849,336 B2). Filed 2020-07-24.
Malboubi, Mehdi et al. (2025). Automatic and real-time cell performance examination and prediction in communication networks (US 12,302,157 B2). Filed 2022-07-14.
Park, Taesuh et al. (2023). Method, electronic device and non-transitory computer-readable storage medium for determining indoor radio transmitter distribution (US 2023/0059954 A1). Filed 2020-05-11.
Other Pertinent References
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Ayyalasomayajula, Sai Roshan et al. (2022). Wireless device localization (US 2022/0196787 A1). Filed 2020-05-01.
Bajorski, Mateusz et al. (2024). Wireless consumer-electronic devices with levitation capabilities (US 2024/0214829 A1). Filed 2022-12-23.
Boccadoro, Pietro et al. (2025). Computer-implemented method for automated planning deployment of radio communication devices in an environment (US 12,245,049 B2). Filed 2022-09-30.
Chatelain, Edward L. et al. (2017). Visual representation of signal strength using machine learning models (US 2017/0280332 A1). Filed 2016-03-24.
Doken, Serhad et al. (2023). Systems and methods for selectively providing wireless signal characteristics to service providers (US 2023/0091437 A1). Filed 2021-09-22.
Ergen, Mustafa et al. (2022). Method and system for managing a plurality of wi-fi access points using a cloud based adaptive software defined network (US 2022/0191713 A1). Filed 2017-12-26.
Michel, François et al. (2023). Method of allocation in an on-board data transmission network in a mobile passenger transport vehicle and associated computer program (US 2023/0126393 A1). Filed 2022-10-25.
Mahalingam, Nagi et al. (2022). Method and apparatus for cbrs network planning and operating in an enterprise network (US 2022/0070682 A1). Filed 2020-09-02.
Oduwaiye, Muhib et al. (2022). Wi-fi access point coordinated transmission of data (US 2022/0174638 A1). Filed 2022-02-14.
Panje, Krishna Prasad et al. (2022). Using machine learning to develop client device test point identify a new position for an access point (ap) (US 2022/0095120 A1). Filed 2021-09-01.
Park, Sungjin et al. (2023). Reuse of space in multi-ap system (US 2023/0093547 A1). Filed 2021-01-04.
Perez-Ramirez, Javier et al. (2020). Automated network control systems that adapt network configurations based on the local network environment (US 2020/0329386 A1). Filed 2020-06-26.
Soma, Dileep Kumar et al. (2021). Systems and methods of room profiling using wireless local area networks (US 2021/0037498 A1). Filed 2019-08-02.
Xu, Hailiang et al. (2019). Positioning a terminal device based on deep learning (US 2019/0353487 A1). Filed 2019-08-01.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SAMUEL H. LEONARD/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649